Deformability based sorting of red blood cells improves diagnostic sensitivity for malaria caused by Plasmodium falciparum
Bibliographic record
Abstract
The loss of red blood cell (RBC) deformability is part of the pathology of many diseases. In malaria caused by Plasmodium falciparum infection, metabolism of hemoglobin by the parasite results in progressive reduction in RBC deformability that is directly correlated with the growth and development of the parasite. The ability to sort RBCs based on deformability therefore provides a means to isolate pathological cells and to study biochemical events associated with disease progression. Existing methods have not been able to sort RBCs based on deformability or to effectively enrich for P. falciparum infected RBCs at clinically relevant concentrations. Here, we develop a method to sort RBCs based on deformability and demonstrate the ability to enrich the concentration of ring-stage P. falciparum infected RBCs (Pf-iRBCs) by >100× from clinically relevant parasitemia (<0.01%). Deformability based sorting of RBCs is accomplished using ratchet transport through asymmetrical constrictions using oscillatory flow. This mechanism provides dramatically improved selectivity over previous biophysical methods by preventing the accumulation of cells in the filter microstructure to ensure that consistent filtration forces are applied to each cell. We show that our approach dramatically improves the sensitivity of malaria diagnosis performed using both microscopy and rapid diagnostic test by converting samples with difficult-to-detect parasitemia (<0.01%) into samples with easily detectable parasitemia (>0.1%).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".